Data Engineering Optimizing Query Performance Questions and Answers

Question 1

A data engineering team manages a massive historical sales data table in a cloud data warehouse.
The table is several terabytes in size, and most analytical queries filter data by `transaction_date` to look at specific months or years.
Queries that scan the entire table are becoming extremely slow and costly.
Which optimization technique would provide the most significant improvement in performance and cost for these date-based queries?

Accepted Answer

Partitioning the table by the `transaction_date` column.

Answer

Implementing row-level security on the table.

Answer

Creating a materialized view that aggregates total daily sales.

Answer

Switching the table's file format from Parquet to Avro.

Question 2

In a distributed query engine like Apache Spark, you are joining a very large fact table (billions of rows) with a small dimension table (a few hundred rows). Which join strategy is the most efficient and should be used by the optimizer in this scenario?

Accepted Answer

Broadcast Hash Join

Answer

Sort Merge Join

Answer

Shuffle Hash Join

Answer

Cartesian Product Join

Question 3

A data engineer is troubleshooting a slow-running SQL query. Which of the following is the primary purpose of analyzing the query's execution plan?

Accepted Answer

To understand the sequence of operations (e.g., scans, joins, aggregations) the database will perform to execute the query.

Answer

To validate the SQL syntax and check for errors before running the query.

Answer

To estimate the financial cost of running the query in a cloud data warehouse.

Answer

To rewrite the query automatically into a more performant version.

Question 4

An analytics team frequently runs queries that aggregate metrics across a few specific columns (e.g., `SUM(revenue)`, `AVG(quantity)`) from a wide table with over 200 columns. The current storage format is row-based (like Avro). Queries are slow because they read a lot of unnecessary data. To improve performance for these specific analytical queries, which change would be most impactful?

Accepted Answer

Switching the storage format to a columnar format like Parquet or ORC.

Answer

Increasing the number of nodes in the processing cluster.

Answer

Denormalizing the data into an even wider table.

Answer

Creating a B-tree index on every one of the 200 columns.

Question 5

What is the primary benefit of 'predicate pushdown' in a modern data warehouse or query engine?

Accepted Answer

It filters data as early as possible in the query execution, often at the storage layer, to reduce the amount of data processed in later stages.

Answer

It ensures that all data is encrypted before being written to disk.

Answer

It allows multiple users to query the same table concurrently without locking issues.

Answer

It automatically caches the results of frequently executed queries.

Question 6

A company's main dashboard runs the same complex, resource-intensive query every 5 minutes to calculate key performance indicators (KPIs) over the last 24 hours. This constant re-computation is straining the data warehouse and increasing costs. The data freshness requirement is that the dashboard can be up to 15 minutes out of date. Which solution offers the best balance of reducing load, lowering cost, and meeting the freshness requirement?

Accepted Answer

Creating a materialized view that is refreshed every 15 minutes.

Answer

Granting more users direct access to run the query on the base tables.

Answer

Rewriting the query using Common Table Expressions (CTEs).

Answer

Increasing the size of the data warehouse cluster.